Overview

Dataset statistics

Number of variables16
Number of observations1073
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory134.3 KiB
Average record size in memory128.1 B

Variable types

Numeric10
Text3
Categorical2
DateTime1

Alerts

disponibilidade has constant value ""Constant
latitude is highly overall correlated with longitude and 1 other fieldsHigh correlation
longitude is highly overall correlated with latitudeHigh correlation
numero_reviews is highly overall correlated with reviews_por_mesHigh correlation
preco is highly overall correlated with latitudeHigh correlation
reviews_por_mes is highly overall correlated with numero_reviews and 1 other fieldsHigh correlation
reviewss_ultimo_mes is highly overall correlated with reviews_por_mesHigh correlation
tipo is highly imbalanced (58.1%)Imbalance
id_imovel has unique valuesUnique
reviewss_ultimo_mes has 566 (52.7%) zerosZeros

Reproduction

Analysis started2024-04-29 20:56:49.939460
Analysis finished2024-04-29 20:57:36.247080
Duration46.31 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

id_imovel
Real number (ℝ)

UNIQUE 

Distinct1073
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4662791 × 1017
Minimum77318
Maximum1.0515477 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-04-29T17:57:36.630971image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum77318
5-th percentile3020086.2
Q117330197
median41902371
Q36.8857447 × 1017
95-th percentile8.8781867 × 1017
Maximum1.0515477 × 1018
Range1.0515477 × 1018
Interquartile range (IQR)6.8857447 × 1017

Descriptive statistics

Standard deviation3.6443176 × 1017
Coefficient of variation (CV)1.4776582
Kurtosis-1.1355567
Mean2.4662791 × 1017
Median Absolute Deviation (MAD)28196170
Skewness0.86074818
Sum6.3773328 × 1018
Variance1.328105 × 1035
MonotonicityNot monotonic
2024-04-29T17:57:37.150952image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13926723 1
 
0.1%
2440469 1
 
0.1%
9.071010474 × 10171
 
0.1%
9.096305877 × 10171
 
0.1%
9.179795333 × 10171
 
0.1%
9.204612128 × 10171
 
0.1%
9.240376626 × 10171
 
0.1%
9.237521833 × 10171
 
0.1%
9.280409295 × 10171
 
0.1%
9.259353979 × 10171
 
0.1%
Other values (1063) 1063
99.1%
ValueCountFrequency (%)
77318 1
0.1%
295955 1
0.1%
330913 1
0.1%
435728 1
0.1%
462947 1
0.1%
483578 1
0.1%
573086 1
0.1%
670109 1
0.1%
681179 1
0.1%
710338 1
0.1%
ValueCountFrequency (%)
1.051547688 × 10181
0.1%
1.022389363 × 10181
0.1%
1.020530394 × 10181
0.1%
1.016576333 × 10181
0.1%
1.010905172 × 10181
0.1%
1.009889631 × 10181
0.1%
1.003697341 × 10181
0.1%
9.987896482 × 10171
0.1%
9.986964432 × 10171
0.1%
9.963775646 × 10171
0.1%

nome
Text

Distinct814
Distinct (%)75.9%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
2024-04-29T17:57:37.799537image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length92
Median length83
Mean length67.55918
Min length45

Characters and Unicode

Total characters72491
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique728 ?
Unique (%)67.8%

Sample

1st rowHome in Rio de Janeiro · 5.0 · 1 quarto · 1 cama · 1 banheiro
2nd rowCabin in Gávea · 4.0 · 1 quarto · 1 cama · 2 banheiros
3rd rowCondo in Recreio dos Bandeirantes · 1 quarto · 3 camas · 1 banheiro
4th rowRental unit in Rio de Janeiro · 1 quarto · 1 cama · 1 banheiro compartilhado
5th rowRental unit in Rio de Janeiro · 2 quartos · 3 camas · 1 banheiro
ValueCountFrequency (%)
· 3930
23.6%
1 1694
 
10.2%
in 1073
 
6.4%
rental 824
 
4.9%
unit 824
 
4.9%
2 821
 
4.9%
banheiro 688
 
4.1%
camas 635
 
3.8%
quarto 613
 
3.7%
rio 609
 
3.7%
Other values (187) 4965
29.8%
2024-04-29T17:57:39.065915image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15617
21.5%
a 7256
 
10.0%
n 4854
 
6.7%
i 4647
 
6.4%
o 4296
 
5.9%
· 3930
 
5.4%
e 3665
 
5.1%
r 3232
 
4.5%
t 3189
 
4.4%
u 1998
 
2.8%
Other values (65) 19807
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72491
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15617
21.5%
a 7256
 
10.0%
n 4854
 
6.7%
i 4647
 
6.4%
o 4296
 
5.9%
· 3930
 
5.4%
e 3665
 
5.1%
r 3232
 
4.5%
t 3189
 
4.4%
u 1998
 
2.8%
Other values (65) 19807
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72491
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15617
21.5%
a 7256
 
10.0%
n 4854
 
6.7%
i 4647
 
6.4%
o 4296
 
5.9%
· 3930
 
5.4%
e 3665
 
5.1%
r 3232
 
4.5%
t 3189
 
4.4%
u 1998
 
2.8%
Other values (65) 19807
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72491
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15617
21.5%
a 7256
 
10.0%
n 4854
 
6.7%
i 4647
 
6.4%
o 4296
 
5.9%
· 3930
 
5.4%
e 3665
 
5.1%
r 3232
 
4.5%
t 3189
 
4.4%
u 1998
 
2.8%
Other values (65) 19807
27.3%

host_id
Real number (ℝ)

Distinct954
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3702023 × 108
Minimum11739
Maximum5.4505559 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-04-29T17:57:39.797624image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum11739
5-th percentile3678983.6
Q122713969
median74337848
Q32.2746344 × 108
95-th percentile4.542476 × 108
Maximum5.4505559 × 108
Range5.4504385 × 108
Interquartile range (IQR)2.0474947 × 108

Descriptive statistics

Standard deviation1.4474169 × 108
Coefficient of variation (CV)1.0563526
Kurtosis-0.043378689
Mean1.3702023 × 108
Median Absolute Deviation (MAD)62823987
Skewness1.0847596
Sum1.4702271 × 1011
Variance2.0950156 × 1016
MonotonicityNot monotonic
2024-04-29T17:57:40.389143image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
292590334 17
 
1.6%
315740743 9
 
0.8%
22713969 5
 
0.5%
172482654 4
 
0.4%
271367467 4
 
0.4%
27229964 3
 
0.3%
58474569 3
 
0.3%
6000862 3
 
0.3%
15925374 3
 
0.3%
316737705 3
 
0.3%
Other values (944) 1019
95.0%
ValueCountFrequency (%)
11739 2
0.2%
110002 1
0.1%
153721 1
0.1%
198706 1
0.1%
403781 1
0.1%
453307 1
0.1%
500892 1
0.1%
533666 1
0.1%
652180 1
0.1%
690247 1
0.1%
ValueCountFrequency (%)
545055587 1
0.1%
519053877 2
0.2%
513697681 1
0.1%
510549225 1
0.1%
509479873 1
0.1%
505338658 1
0.1%
502278654 1
0.1%
500818847 1
0.1%
500459166 1
0.1%
497921624 1
0.1%
Distinct590
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
2024-04-29T17:57:41.070050image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length31
Median length27
Mean length7.1817335
Min length2

Characters and Unicode

Total characters7706
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique407 ?
Unique (%)37.9%

Sample

1st rowGlauco
2nd rowAmanda
3rd rowGizella
4th rowThiago
5th rowLiliane Marques
ValueCountFrequency (%)
maria 29
 
2.3%
ana 27
 
2.1%
belsito 17
 
1.4%
apartments 17
 
1.4%
marcelo 16
 
1.3%
luiz 14
 
1.1%
pedro 11
 
0.9%
carolina 10
 
0.8%
paula 10
 
0.8%
claudio 9
 
0.7%
Other values (572) 1096
87.3%
2024-04-29T17:57:42.121327image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1215
15.8%
i 726
 
9.4%
e 606
 
7.9%
n 540
 
7.0%
r 530
 
6.9%
l 486
 
6.3%
o 477
 
6.2%
s 268
 
3.5%
u 225
 
2.9%
t 195
 
2.5%
Other values (57) 2438
31.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7706
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1215
15.8%
i 726
 
9.4%
e 606
 
7.9%
n 540
 
7.0%
r 530
 
6.9%
l 486
 
6.3%
o 477
 
6.2%
s 268
 
3.5%
u 225
 
2.9%
t 195
 
2.5%
Other values (57) 2438
31.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7706
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1215
15.8%
i 726
 
9.4%
e 606
 
7.9%
n 540
 
7.0%
r 530
 
6.9%
l 486
 
6.3%
o 477
 
6.2%
s 268
 
3.5%
u 225
 
2.9%
t 195
 
2.5%
Other values (57) 2438
31.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7706
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1215
15.8%
i 726
 
9.4%
e 606
 
7.9%
n 540
 
7.0%
r 530
 
6.9%
l 486
 
6.3%
o 477
 
6.2%
s 268
 
3.5%
u 225
 
2.9%
t 195
 
2.5%
Other values (57) 2438
31.6%

bairro
Text

Distinct61
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
2024-04-29T17:57:42.550326image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length24
Median length20
Mean length10.200373
Min length4

Characters and Unicode

Total characters10945
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)2.1%

Sample

1st rowHumaitá
2nd rowGávea
3rd rowRecreio dos Bandeirantes
4th rowRio Comprido
5th rowJacarepaguá
ValueCountFrequency (%)
copacabana 341
23.0%
tijuca 113
 
7.6%
barra 108
 
7.3%
da 107
 
7.2%
ipanema 79
 
5.3%
botafogo 59
 
4.0%
jacarepaguá 59
 
4.0%
leblon 53
 
3.6%
recreio 48
 
3.2%
dos 48
 
3.2%
Other values (72) 469
31.6%
2024-04-29T17:57:43.444052image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2742
25.1%
o 834
 
7.6%
n 749
 
6.8%
e 687
 
6.3%
r 599
 
5.5%
c 594
 
5.4%
p 481
 
4.4%
C 429
 
3.9%
b 411
 
3.8%
411
 
3.8%
Other values (43) 3008
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10945
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2742
25.1%
o 834
 
7.6%
n 749
 
6.8%
e 687
 
6.3%
r 599
 
5.5%
c 594
 
5.4%
p 481
 
4.4%
C 429
 
3.9%
b 411
 
3.8%
411
 
3.8%
Other values (43) 3008
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10945
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2742
25.1%
o 834
 
7.6%
n 749
 
6.8%
e 687
 
6.3%
r 599
 
5.5%
c 594
 
5.4%
p 481
 
4.4%
C 429
 
3.9%
b 411
 
3.8%
411
 
3.8%
Other values (43) 3008
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10945
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2742
25.1%
o 834
 
7.6%
n 749
 
6.8%
e 687
 
6.3%
r 599
 
5.5%
c 594
 
5.4%
p 481
 
4.4%
C 429
 
3.9%
b 411
 
3.8%
411
 
3.8%
Other values (43) 3008
27.5%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct1013
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-22.965852
Minimum-23.064618
Maximum-22.74969
Zeros0
Zeros (%)0.0%
Negative1073
Negative (%)100.0%
Memory size8.5 KiB
2024-04-29T17:57:43.866143image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-23.064618
5-th percentile-23.012142
Q1-22.98359
median-22.96975
Q3-22.95148
95-th percentile-22.91271
Maximum-22.74969
Range0.314928
Interquartile range (IQR)0.03211

Descriptive statistics

Standard deviation0.033020379
Coefficient of variation (CV)-0.0014378034
Kurtosis4.7990202
Mean-22.965852
Median Absolute Deviation (MAD)0.014619
Skewness1.2074981
Sum-24642.359
Variance0.0010903455
MonotonicityNot monotonic
2024-04-29T17:57:44.326121image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-22.97716 4
 
0.4%
-22.96471 4
 
0.4%
-22.962423 3
 
0.3%
-22.96941 3
 
0.3%
-22.97358 3
 
0.3%
-22.9831 3
 
0.3%
-22.99804 3
 
0.3%
-22.9689 3
 
0.3%
-22.97909 2
 
0.2%
-22.96411 2
 
0.2%
Other values (1003) 1043
97.2%
ValueCountFrequency (%)
-23.064618 1
0.1%
-23.06415 1
0.1%
-23.05091 1
0.1%
-23.03599 1
0.1%
-23.031011 1
0.1%
-23.03092 1
0.1%
-23.03005 1
0.1%
-23.03002 1
0.1%
-23.02971 1
0.1%
-23.02831 1
0.1%
ValueCountFrequency (%)
-22.74969 1
0.1%
-22.75295 1
0.1%
-22.805221 1
0.1%
-22.806724 1
0.1%
-22.81064 1
0.1%
-22.8118 1
0.1%
-22.8179 1
0.1%
-22.84107 1
0.1%
-22.86509 1
0.1%
-22.877013 1
0.1%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct1010
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-43.23803
Minimum-43.690056
Maximum-43.10605
Zeros0
Zeros (%)0.0%
Negative1073
Negative (%)100.0%
Memory size8.5 KiB
2024-04-29T17:57:44.824489image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-43.690056
5-th percentile-43.443278
Q1-43.23766
median-43.1919
Q3-43.18325
95-th percentile-43.174356
Maximum-43.10605
Range0.584006
Interquartile range (IQR)0.05441

Descriptive statistics

Standard deviation0.089877896
Coefficient of variation (CV)-0.0020786769
Kurtosis1.5970671
Mean-43.23803
Median Absolute Deviation (MAD)0.01349
Skewness-1.6087941
Sum-46394.407
Variance0.0080780362
MonotonicityNot monotonic
2024-04-29T17:57:45.317889image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-43.19046 4
 
0.4%
-43.17932 3
 
0.3%
-43.192 3
 
0.3%
-43.188864 3
 
0.3%
-43.19107 3
 
0.3%
-43.175888 3
 
0.3%
-43.256821 3
 
0.3%
-43.19054 2
 
0.2%
-43.18346 2
 
0.2%
-43.18214 2
 
0.2%
Other values (1000) 1045
97.4%
ValueCountFrequency (%)
-43.690056 1
0.1%
-43.566976 1
0.1%
-43.56487 1
0.1%
-43.55281 1
0.1%
-43.55113 1
0.1%
-43.54527 1
0.1%
-43.51179 1
0.1%
-43.49576 1
0.1%
-43.49481 1
0.1%
-43.49304 1
0.1%
ValueCountFrequency (%)
-43.10605 1
0.1%
-43.10803 1
0.1%
-43.16123 1
0.1%
-43.161297 1
0.1%
-43.1616 1
0.1%
-43.16282 1
0.1%
-43.16396 1
0.1%
-43.16641 1
0.1%
-43.16762 1
0.1%
-43.167629 1
0.1%

tipo
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
casa/apartamento
825 
quarto privado
239 
quarto compartilhado
 
7
quarto de hotel
 
2

Length

Max length20
Median length16
Mean length15.578751
Min length14

Characters and Unicode

Total characters16716
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcasa/apartamento
2nd rowcasa/apartamento
3rd rowcasa/apartamento
4th rowquarto privado
5th rowcasa/apartamento

Common Values

ValueCountFrequency (%)
casa/apartamento 825
76.9%
quarto privado 239
 
22.3%
quarto compartilhado 7
 
0.7%
quarto de hotel 2
 
0.2%

Length

2024-04-29T17:57:45.722000image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-29T17:57:46.061621image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
casa/apartamento 825
62.4%
quarto 248
 
18.7%
privado 239
 
18.1%
compartilhado 7
 
0.5%
de 2
 
0.2%
hotel 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a 4626
27.7%
t 1907
11.4%
o 1328
 
7.9%
r 1319
 
7.9%
p 1071
 
6.4%
c 832
 
5.0%
m 832
 
5.0%
e 829
 
5.0%
n 825
 
4.9%
/ 825
 
4.9%
Other values (9) 2322
13.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16716
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4626
27.7%
t 1907
11.4%
o 1328
 
7.9%
r 1319
 
7.9%
p 1071
 
6.4%
c 832
 
5.0%
m 832
 
5.0%
e 829
 
5.0%
n 825
 
4.9%
/ 825
 
4.9%
Other values (9) 2322
13.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16716
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4626
27.7%
t 1907
11.4%
o 1328
 
7.9%
r 1319
 
7.9%
p 1071
 
6.4%
c 832
 
5.0%
m 832
 
5.0%
e 829
 
5.0%
n 825
 
4.9%
/ 825
 
4.9%
Other values (9) 2322
13.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16716
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4626
27.7%
t 1907
11.4%
o 1328
 
7.9%
r 1319
 
7.9%
p 1071
 
6.4%
c 832
 
5.0%
m 832
 
5.0%
e 829
 
5.0%
n 825
 
4.9%
/ 825
 
4.9%
Other values (9) 2322
13.9%

preco
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1167.7542
Minimum142.4
Maximum8674.7792
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-04-29T17:57:46.458661image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum142.4
5-th percentile549.53689
Q1785.29567
median1311.7262
Q31311.7262
95-th percentile1726.5436
Maximum8674.7792
Range8532.3792
Interquartile range (IQR)526.43056

Descriptive statistics

Standard deviation629.54468
Coefficient of variation (CV)0.53910717
Kurtosis90.266377
Mean1167.7542
Median Absolute Deviation (MAD)252.12293
Skewness7.7436578
Sum1253000.3
Variance396326.51
MonotonicityNot monotonic
2024-04-29T17:57:46.930442image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1311.726228 341
31.8%
1350.845188 103
 
9.6%
1726.543616 79
 
7.4%
768.3704453 59
 
5.5%
785.2956685 57
 
5.3%
1547.793061 53
 
4.9%
962.8882455 48
 
4.5%
549.5368889 38
 
3.5%
748.8612132 36
 
3.4%
678.5729443 32
 
3.0%
Other values (51) 227
21.2%
ValueCountFrequency (%)
142.4 1
 
0.1%
158.6153846 1
 
0.1%
245 1
 
0.1%
268.7083333 2
0.2%
271.8333333 2
0.2%
298.4444444 4
0.4%
318.03125 1
 
0.1%
357.6724138 4
0.4%
368.1111111 1
 
0.1%
371 1
 
0.1%
ValueCountFrequency (%)
8674.779221 4
 
0.4%
8478.032787 1
 
0.1%
2487.029326 6
 
0.6%
1938.118012 2
 
0.2%
1770 1
 
0.1%
1726.543616 79
7.4%
1567.038911 9
 
0.8%
1547.793061 53
4.9%
1350.845188 103
9.6%
1337.539171 7
 
0.7%

noites_minimas
Real number (ℝ)

Distinct23
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.904778
Minimum1
Maximum365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-04-29T17:57:47.299496image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile15
Maximum365
Range364
Interquartile range (IQR)2

Descriptive statistics

Standard deviation16.782862
Coefficient of variation (CV)3.4217374
Kurtosis304.03637
Mean4.904778
Median Absolute Deviation (MAD)1
Skewness16.147924
Sum5262.8268
Variance281.66446
MonotonicityNot monotonic
2024-04-29T17:57:47.628013image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2 333
31.0%
3 246
22.9%
1 189
17.6%
4 97
 
9.0%
5 92
 
8.6%
7 27
 
2.5%
30 19
 
1.8%
6 18
 
1.7%
28 12
 
1.1%
10 11
 
1.0%
Other values (13) 29
 
2.7%
ValueCountFrequency (%)
1 189
17.6%
2 333
31.0%
3 246
22.9%
4 97
 
9.0%
4.826785714 1
 
0.1%
5 92
 
8.6%
6 18
 
1.7%
7 27
 
2.5%
8 2
 
0.2%
10 11
 
1.0%
ValueCountFrequency (%)
365 1
 
0.1%
300 1
 
0.1%
200 1
 
0.1%
90 1
 
0.1%
88 1
 
0.1%
60 1
 
0.1%
30 19
1.8%
28 12
1.1%
27 1
 
0.1%
25 4
 
0.4%

numero_reviews
Real number (ℝ)

HIGH CORRELATION 

Distinct100
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.67288
Minimum1
Maximum355
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-04-29T17:57:48.023489image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q312
95-th percentile61.4
Maximum355
Range354
Interquartile range (IQR)10

Descriptive statistics

Standard deviation27.839763
Coefficient of variation (CV)2.0361302
Kurtosis38.49873
Mean13.67288
Median Absolute Deviation (MAD)3
Skewness5.1625578
Sum14671
Variance775.05241
MonotonicityNot monotonic
2024-04-29T17:57:48.428943image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 215
20.0%
2 149
13.9%
3 109
 
10.2%
4 77
 
7.2%
5 56
 
5.2%
6 52
 
4.8%
7 34
 
3.2%
8 34
 
3.2%
9 29
 
2.7%
14 21
 
2.0%
Other values (90) 297
27.7%
ValueCountFrequency (%)
1 215
20.0%
2 149
13.9%
3 109
10.2%
4 77
 
7.2%
5 56
 
5.2%
6 52
 
4.8%
7 34
 
3.2%
8 34
 
3.2%
9 29
 
2.7%
10 20
 
1.9%
ValueCountFrequency (%)
355 1
0.1%
252 1
0.1%
238 1
0.1%
222 1
0.1%
189 1
0.1%
169 1
0.1%
168 1
0.1%
156 1
0.1%
135 2
0.2%
133 1
0.1%
Distinct512
Distinct (%)47.7%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
Minimum2013-07-21 00:00:00
Maximum2023-12-23 00:00:00
2024-04-29T17:57:48.907539image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:49.725319image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

reviews_por_mes
Real number (ℝ)

HIGH CORRELATION 

Distinct171
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.37944082
Minimum0.01
Maximum7.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-04-29T17:57:50.191439image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.02
Q10.07
median0.16
Q30.43
95-th percentile1.48
Maximum7.11
Range7.1
Interquartile range (IQR)0.36

Descriptive statistics

Standard deviation0.56573874
Coefficient of variation (CV)1.4909802
Kurtosis24.482347
Mean0.37944082
Median Absolute Deviation (MAD)0.12
Skewness3.7140433
Sum407.14
Variance0.32006032
MonotonicityNot monotonic
2024-04-29T17:57:50.634724image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 69
 
6.4%
0.04 68
 
6.3%
0.02 57
 
5.3%
0.08 54
 
5.0%
0.06 52
 
4.8%
0.03 41
 
3.8%
0.13 37
 
3.4%
0.17 34
 
3.2%
0.09 32
 
3.0%
0.19 31
 
2.9%
Other values (161) 598
55.7%
ValueCountFrequency (%)
0.01 16
 
1.5%
0.02 57
5.3%
0.03 41
3.8%
0.04 68
6.3%
0.05 19
 
1.8%
0.06 52
4.8%
0.07 20
 
1.9%
0.08 54
5.0%
0.09 32
3.0%
0.1 69
6.4%
ValueCountFrequency (%)
7.11 1
0.1%
3.62 1
0.1%
3.4 1
0.1%
3.35 1
0.1%
3.21 1
0.1%
3.16 1
0.1%
3.06 1
0.1%
3.01 1
0.1%
2.96 1
0.1%
2.91 1
0.1%

hosts_qtd
Real number (ℝ)

Distinct32
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5750233
Minimum1
Maximum185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-04-29T17:57:51.009976image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile20
Maximum185
Range184
Interquartile range (IQR)1

Descriptive statistics

Standard deviation14.453354
Coefficient of variation (CV)3.159187
Kurtosis91.820289
Mean4.5750233
Median Absolute Deviation (MAD)0
Skewness8.6436296
Sum4909
Variance208.89945
MonotonicityNot monotonic
2024-04-29T17:57:51.417128image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 659
61.4%
2 186
 
17.3%
3 53
 
4.9%
4 35
 
3.3%
41 18
 
1.7%
5 13
 
1.2%
9 11
 
1.0%
6 10
 
0.9%
22 10
 
0.9%
7 9
 
0.8%
Other values (22) 69
 
6.4%
ValueCountFrequency (%)
1 659
61.4%
2 186
 
17.3%
3 53
 
4.9%
4 35
 
3.3%
5 13
 
1.2%
6 10
 
0.9%
7 9
 
0.8%
8 5
 
0.5%
9 11
 
1.0%
10 7
 
0.7%
ValueCountFrequency (%)
185 3
 
0.3%
145 2
 
0.2%
142 1
 
0.1%
80 1
 
0.1%
56 2
 
0.2%
47 2
 
0.2%
41 18
1.7%
38 5
 
0.5%
35 3
 
0.3%
33 2
 
0.2%

disponibilidade
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.5 KiB
0
1073 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1073
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1073
100.0%

Length

2024-04-29T17:57:52.225798image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-29T17:57:52.765191image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1073
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1073
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1073
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1073
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1073
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1073
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1073
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1073
100.0%

reviewss_ultimo_mes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5163094
Minimum0
Maximum53
Zeros566
Zeros (%)52.7%
Negative0
Negative (%)0.0%
Memory size8.5 KiB
2024-04-29T17:57:53.111041image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile13
Maximum53
Range53
Interquartile range (IQR)2

Descriptive statistics

Standard deviation5.6315645
Coefficient of variation (CV)2.2380255
Kurtosis24.060137
Mean2.5163094
Median Absolute Deviation (MAD)0
Skewness4.2852281
Sum2700
Variance31.714519
MonotonicityNot monotonic
2024-04-29T17:57:53.493220image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 566
52.7%
1 170
 
15.8%
2 83
 
7.7%
3 52
 
4.8%
4 29
 
2.7%
6 28
 
2.6%
5 27
 
2.5%
7 16
 
1.5%
8 14
 
1.3%
9 11
 
1.0%
Other values (28) 77
 
7.2%
ValueCountFrequency (%)
0 566
52.7%
1 170
 
15.8%
2 83
 
7.7%
3 52
 
4.8%
4 29
 
2.7%
5 27
 
2.5%
6 28
 
2.6%
7 16
 
1.5%
8 14
 
1.3%
9 11
 
1.0%
ValueCountFrequency (%)
53 1
0.1%
49 1
0.1%
47 1
0.1%
45 1
0.1%
42 1
0.1%
33 2
0.2%
32 1
0.1%
31 1
0.1%
29 1
0.1%
28 2
0.2%

Interactions

2024-04-29T17:57:31.498784image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:56:51.074594image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:56:54.892028image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:00.164948image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:05.041293image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:09.925133image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:16.515895image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:20.203557image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:23.736190image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:27.820160image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:31.848711image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:56:51.369749image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:56:55.928396image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:00.701411image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:05.403589image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:10.489146image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:17.033997image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:20.525402image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:24.035037image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:28.195897image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:32.136797image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:56:51.683993image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:56:56.280655image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:01.770742image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:05.707991image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:11.124442image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:17.468004image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:20.835091image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:24.352205image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:28.468182image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:32.465425image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:56:52.004857image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:56:56.936908image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:02.166142image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:06.069467image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:12.244540image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:17.829647image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:21.201153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:24.652152image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:28.779807image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:32.841182image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:56:52.378644image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:56:57.664993image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:02.897807image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:06.399668image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:12.630188image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:18.196796image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:21.549170image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:25.061939image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:29.080796image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:33.191668image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:56:52.865785image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:56:58.188571image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:03.266206image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:06.950132image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:12.908233image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:18.508154image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:21.858174image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:25.510589image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:29.396940image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:33.641079image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:56:53.320211image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:56:58.643206image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:03.585424image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:07.725184image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:13.246868image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:18.819913image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:22.307540image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:26.006755image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:30.148941image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:34.047554image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:56:53.723203image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:56:58.980546image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:03.900903image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:08.147554image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:14.036668image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:19.275972image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:22.798501image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:26.510596image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:30.480927image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:34.395841image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:56:54.010526image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:56:59.323236image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:04.230183image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:08.816239image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:14.764009image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:19.599178image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:23.128864image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:26.904556image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:30.794198image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:34.791275image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:56:54.328274image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:56:59.639829image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:04.695442image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:09.456485image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:15.728991image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:19.931874image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:23.428496image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:27.418919image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-29T17:57:31.147037image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-04-29T17:57:53.798740image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
host_idhosts_qtdid_imovellatitudelongitudenoites_minimasnumero_reviewsprecoreviews_por_mesreviewss_ultimo_mestipo
host_id1.000-0.0110.455-0.046-0.062-0.150-0.074-0.0680.1660.2010.000
hosts_qtd-0.0111.0000.167-0.085-0.0300.044-0.0520.1220.0710.1210.000
id_imovel0.4550.1671.000-0.032-0.015-0.194-0.305-0.0030.3250.4510.022
latitude-0.046-0.085-0.0321.0000.601-0.012-0.001-0.709-0.019-0.0190.097
longitude-0.062-0.030-0.0150.6011.0000.1350.104-0.2480.0860.0890.058
noites_minimas-0.1500.044-0.194-0.0120.1351.000-0.0050.125-0.129-0.1360.000
numero_reviews-0.074-0.052-0.305-0.0010.104-0.0051.0000.0470.7150.2880.000
preco-0.0680.122-0.003-0.709-0.2480.1250.0471.0000.0570.0650.076
reviews_por_mes0.1660.0710.325-0.0190.086-0.1290.7150.0571.0000.6370.000
reviewss_ultimo_mes0.2010.1210.451-0.0190.089-0.1360.2880.0650.6371.0000.000
tipo0.0000.0000.0220.0970.0580.0000.0000.0760.0000.0001.000

Missing values

2024-04-29T17:57:35.221733image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-29T17:57:35.932923image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

id_imovelnomehost_idhost_nomebairrolatitudelongitudetipopreconoites_minimasnumero_reviewsultima_reviewreviews_por_meshosts_qtddisponibilidadereviewss_ultimo_mes
013926723Home in Rio de Janeiro · 5.0 · 1 quarto · 1 cama · 1 banheiro21482982GlaucoHumaitá-22.952720-43.198410casa/apartamento795.20430127.0132023-08-100.14301
1564919861942861050Cabin in Gávea · 4.0 · 1 quarto · 1 cama · 2 banheiros363844454AmandaGávea-22.980390-43.241180casa/apartamento1337.5391711.042023-04-230.18103
24250213Condo in Recreio dos Bandeirantes · 1 quarto · 3 camas · 1 banheiro16203836GizellaRecreio dos Bandeirantes-23.030920-43.474190casa/apartamento962.8882463.022022-11-150.13100
34854025Rental unit in Rio de Janeiro · 1 quarto · 1 cama · 1 banheiro compartilhado24971450ThiagoRio Comprido-22.925016-43.201073quarto privado553.3669722.012021-07-300.03100
45083946Rental unit in Rio de Janeiro · 2 quartos · 3 camas · 1 banheiro26259326Liliane MarquesJacarepaguá-22.969410-43.397750casa/apartamento785.2956694.012019-09-300.02100
55278558Cottage in Rio de Janeiro · 3 quartos · 13 camas · 3.5 banheiros9819895João PedroVargem Grande-22.970870-43.494810casa/apartamento932.8235291.022019-12-290.03400
65820997Rental unit in Rio de Janeiro · 1 quarto · 1 cama · 1 banheiro30210745Maurício E NatáliaGrajaú-22.920072-43.269055casa/apartamento374.2115383.022023-02-210.17102
76039756Condo in Rio de Janeiro · 1 quarto · 1 cama · 1 private banheiro5014163AndersonBarra da Tijuca-23.009060-43.322700quarto privado1350.8451882.022017-09-240.02200
87272073Rental unit in Rio de Janeiro · Studio · 3 camas · 1 banheiro38074964RogerioFlamengo-22.939090-43.174490casa/apartamento678.5729443.012016-02-110.01100
97323556Rental unit in Rio de Janeiro · 2 quartos · 3 camas · 1 banheiro38364611MariaCopacabana-22.977120-43.188550casa/apartamento1311.7262283.022016-01-050.02400
id_imovelnomehost_idhost_nomebairrolatitudelongitudetipopreconoites_minimasnumero_reviewsultima_reviewreviews_por_meshosts_qtddisponibilidadereviewss_ultimo_mes
1063969793423437579937Rental unit in Rio de Janeiro · 5.0 · 1 quarto · 1 cama · 2 banheiros92415018ElaineBarra da Tijuca-23.011537-43.306968casa/apartamento1350.8451882.032023-12-141.61103
1064980317076344793068Rental unit in Rio de Janeiro · 5.0 · 1 quarto · 4 camas · 1 banheiro154114238BrenoRecreio dos Bandeirantes-23.031011-43.475893casa/apartamento962.8882462.0232023-12-237.114023
1065998696443198877884Rental unit in Rio de Janeiro · 5.0 · 1 quarto · 6 camas · 2 banheiro compartilhados164177980FláviaMéier-22.900972-43.275702quarto compartilhado298.4444441.032023-11-201.701803
1066996377564577497393Aparthotel in Rio de Janeiro · 5.0 · 2 quartos · 2 camas · 1 banheiro13323106AllanLaranjeiras-22.940860-43.192000casa/apartamento632.64885525.032023-10-311.451703
10671010905172145107938Home in Rio de Janeiro · New · 3 quartos · 6 camas · 2 banheiros513697681EvelynJardim Carioca-22.806724-43.186281casa/apartamento158.6153851.012023-11-040.56101
10681009889631384884498Rental unit in Rio de Janeiro · New · 3 quartos · 4 camas · 3 banheiros23772370UmerLagoa-22.977202-43.196998casa/apartamento1567.0389113.012023-11-210.83101
10691016576332553624804Rental unit in Rio de Janeiro · New · 1 quarto · 2 camas · 1 banheiro23217266SilviaBarra da Tijuca-23.005797-43.319997casa/apartamento1350.8451882.012023-11-210.81201
10701020530393519858555Rental unit in Rio de Janeiro · New · 1 quarto · 1 cama · 1 banheiro23217266SilviaBarra da Tijuca-23.005797-43.319997casa/apartamento1350.8451882.022023-11-151.30202
10711022389363072197291Rental unit in Rio de Janeiro · New · 2 quartos · 2 camas · 1.5 banheiros15275005JonasCopacabana-22.965095-43.181219casa/apartamento1311.7262282.012023-11-190.771401
10721051547687623181239Rental unit in Rio de Janeiro · New · 1 quarto · 1 cama · 1 banheiro545055587RosangelaPraça da Bandeira-22.914362-43.211230casa/apartamento676.6034481.022023-12-232.00102